The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency response
In situations where natural disasters damage public communication networks, self-organized emergency communication networks play a vital role as important resources for disaster monitoring and emergency response. Geographical conditions, communication capacity, power availability, terminals’ positio...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224000621 |
| _version_ | 1797224340611137536 |
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| author | Yuhui Zeng Xicheng Tan Moquan Sha Zeenat Khadim Hussain Tongliang Lin Jianguang Tu Huamin Wang Bocai Liu Chaopeng Li Fang Huang Zongyao Sha |
| author_facet | Yuhui Zeng Xicheng Tan Moquan Sha Zeenat Khadim Hussain Tongliang Lin Jianguang Tu Huamin Wang Bocai Liu Chaopeng Li Fang Huang Zongyao Sha |
| author_sort | Yuhui Zeng |
| collection | DOAJ |
| description | In situations where natural disasters damage public communication networks, self-organized emergency communication networks play a vital role as important resources for disaster monitoring and emergency response. Geographical conditions, communication capacity, power availability, terminals’ position changes during disasters, and data volume, on the other hand, have a direct impact on emergency terminals’ effectiveness in disaster monitoring and data processing. As a result, the ability to detect calamities quickly and give real-time reactions suffers. To ensure effective emergency communication coverage when public networks are disrupted, intelligent, rapid and reliable optimization of the spatiotemporal dynamic deployment of Ad Hoc Network (ANET) is crucial for disaster monitoring and emergency response. The Deep Deterministic Policy Gradient (DDPG) method is used in this study to assist the spatiotemporal dynamic deployment of air-ground ANET (AGANET) nodes, including unmanned aerial vehicle (UAV) ANET nodes and ground-based ANET nodes. Its goal is to develop a reliable model for ensuring the deployment of AGANET nodes for multi-terminal disaster monitoring. The paper describes the spatiotemporal dynamic deployment of AGANET nodes and develops a reinforcement learning model. Subsequently, it describes a DDPG reinforcement-based optimization method for the spatiotemporal dynamic deployment of AGANET nodes. Specifically, this method includes a greedy matching strategy based on real-time environmental information of AGANET nodes and ground-based sensing terminals, a DDPG AGANET node three-dimensional initialization algorithm, and a DDPG dynamic solution algorithm for spatiotemporal dynamic deployment reinforcement learning model of AGANET. AGANET nodes and ground-based sensing terminals can all work together in this way to provide high-quality emergency AGANET services. When compared to traditional methods, it offers better communication dependability, enhanced efficiency, and cheaper costs associated with building emergency networks. It is also crucial for emergency response in instances where the public network fails. |
| first_indexed | 2024-03-07T23:39:24Z |
| format | Article |
| id | doaj.art-7922bd7ac1994ebdadf315115bb0740c |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-24T13:51:34Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-7922bd7ac1994ebdadf315115bb0740c2024-04-04T05:03:34ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-04-01128103708The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency responseYuhui Zeng0Xicheng Tan1Moquan Sha2Zeenat Khadim Hussain3Tongliang Lin4Jianguang Tu5Huamin Wang6Bocai Liu7Chaopeng Li8Fang Huang9Zongyao Sha10Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, China; School of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, China; Corresponding author.China Unicom Smart City Res Inst, Beijing 100048, Peoples R ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaSchool of Resources & Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Ave, Chengdu 611731, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaIn situations where natural disasters damage public communication networks, self-organized emergency communication networks play a vital role as important resources for disaster monitoring and emergency response. Geographical conditions, communication capacity, power availability, terminals’ position changes during disasters, and data volume, on the other hand, have a direct impact on emergency terminals’ effectiveness in disaster monitoring and data processing. As a result, the ability to detect calamities quickly and give real-time reactions suffers. To ensure effective emergency communication coverage when public networks are disrupted, intelligent, rapid and reliable optimization of the spatiotemporal dynamic deployment of Ad Hoc Network (ANET) is crucial for disaster monitoring and emergency response. The Deep Deterministic Policy Gradient (DDPG) method is used in this study to assist the spatiotemporal dynamic deployment of air-ground ANET (AGANET) nodes, including unmanned aerial vehicle (UAV) ANET nodes and ground-based ANET nodes. Its goal is to develop a reliable model for ensuring the deployment of AGANET nodes for multi-terminal disaster monitoring. The paper describes the spatiotemporal dynamic deployment of AGANET nodes and develops a reinforcement learning model. Subsequently, it describes a DDPG reinforcement-based optimization method for the spatiotemporal dynamic deployment of AGANET nodes. Specifically, this method includes a greedy matching strategy based on real-time environmental information of AGANET nodes and ground-based sensing terminals, a DDPG AGANET node three-dimensional initialization algorithm, and a DDPG dynamic solution algorithm for spatiotemporal dynamic deployment reinforcement learning model of AGANET. AGANET nodes and ground-based sensing terminals can all work together in this way to provide high-quality emergency AGANET services. When compared to traditional methods, it offers better communication dependability, enhanced efficiency, and cheaper costs associated with building emergency networks. It is also crucial for emergency response in instances where the public network fails.http://www.sciencedirect.com/science/article/pii/S1569843224000621Deep Reinforcement LearningAd Hoc NetworkSpatiotemporal OptimizationArtificial IntelligenceDisaster MonitoringEmergency Response |
| spellingShingle | Yuhui Zeng Xicheng Tan Moquan Sha Zeenat Khadim Hussain Tongliang Lin Jianguang Tu Huamin Wang Bocai Liu Chaopeng Li Fang Huang Zongyao Sha The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency response International Journal of Applied Earth Observations and Geoinformation Deep Reinforcement Learning Ad Hoc Network Spatiotemporal Optimization Artificial Intelligence Disaster Monitoring Emergency Response |
| title | The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency response |
| title_full | The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency response |
| title_fullStr | The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency response |
| title_full_unstemmed | The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency response |
| title_short | The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency response |
| title_sort | study of ddpg based spatiotemporal dynamic deployment optimization of air ground ad hoc network for disaster emergency response |
| topic | Deep Reinforcement Learning Ad Hoc Network Spatiotemporal Optimization Artificial Intelligence Disaster Monitoring Emergency Response |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224000621 |
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